Overview

Brought to you by YData

Dataset statistics

Number of variables28
Number of observations1005028
Missing cells5591668
Missing cells (%)19.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory871.4 MiB
Average record size in memory909.2 B

Variable types

Numeric14
DateTime2
Categorical6
Text6

Alerts

AREA is highly overall correlated with AREA NAME and 1 other fieldsHigh correlation
AREA NAME is highly overall correlated with AREA and 1 other fieldsHigh correlation
Crm Cd is highly overall correlated with Crm Cd 1 and 1 other fieldsHigh correlation
Crm Cd 1 is highly overall correlated with Crm Cd and 1 other fieldsHigh correlation
LAT is highly overall correlated with LONHigh correlation
LON is highly overall correlated with LATHigh correlation
Part 1-2 is highly overall correlated with Crm Cd and 2 other fieldsHigh correlation
Rpt Dist No is highly overall correlated with AREA and 1 other fieldsHigh correlation
Status is highly overall correlated with Status DescHigh correlation
Status Desc is highly overall correlated with StatusHigh correlation
Vict Descent is highly overall correlated with Vict SexHigh correlation
Vict Sex is highly overall correlated with Vict DescentHigh correlation
Weapon Used Cd is highly overall correlated with Part 1-2High correlation
Status is highly imbalanced (63.1%) Imbalance
Status Desc is highly imbalanced (63.1%) Imbalance
Mocodes has 151684 (15.1%) missing values Missing
Vict Sex has 144694 (14.4%) missing values Missing
Vict Descent has 144706 (14.4%) missing values Missing
Weapon Used Cd has 677800 (67.4%) missing values Missing
Weapon Desc has 677800 (67.4%) missing values Missing
Crm Cd 2 has 935892 (93.1%) missing values Missing
Crm Cd 3 has 1002713 (99.8%) missing values Missing
Crm Cd 4 has 1004964 (> 99.9%) missing values Missing
Cross Street has 850799 (84.7%) missing values Missing
LAT is highly skewed (γ1 = -20.9601707) Skewed
LON is highly skewed (γ1 = 21.09994479) Skewed
DR_NO has unique values Unique
Vict Age has 269258 (26.8%) zeros Zeros

Reproduction

Analysis started2025-03-07 13:18:03.076165
Analysis finished2025-03-07 13:18:51.446027
Duration48.37 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

DR_NO
Real number (ℝ)

Unique 

Distinct1005028
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.202227 × 108
Minimum817
Maximum2.5210405 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2025-03-07T14:18:51.520718image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum817
5-th percentile2.0060992 × 108
Q12.1061687 × 108
median2.2091594 × 108
Q32.3111033 × 108
95-th percentile2.4140481 × 108
Maximum2.5210405 × 108
Range2.5210324 × 108
Interquartile range (IQR)20493456

Descriptive statistics

Standard deviation13193815
Coefficient of variation (CV)0.059911239
Kurtosis-0.69117876
Mean2.202227 × 108
Median Absolute Deviation (MAD)10209442
Skewness-0.011730722
Sum2.2132998 × 1014
Variance1.7407676 × 1014
MonotonicityNot monotonic
2025-03-07T14:18:51.580634image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
190326475 1
 
< 0.1%
231106173 1
 
< 0.1%
230407793 1
 
< 0.1%
231214657 1
 
< 0.1%
231114059 1
 
< 0.1%
231707200 1
 
< 0.1%
231012146 1
 
< 0.1%
230318409 1
 
< 0.1%
230608923 1
 
< 0.1%
232006266 1
 
< 0.1%
Other values (1005018) 1005018
> 99.9%
ValueCountFrequency (%)
817 1
< 0.1%
2113 1
< 0.1%
2203 1
< 0.1%
2315 1
< 0.1%
2401 1
< 0.1%
10304468 1
< 0.1%
190101086 1
< 0.1%
190101087 1
< 0.1%
190326475 1
< 0.1%
191501505 1
< 0.1%
ValueCountFrequency (%)
252104053 1
< 0.1%
252104040 1
< 0.1%
252104036 1
< 0.1%
252104024 1
< 0.1%
252104023 1
< 0.1%
252104018 1
< 0.1%
252104017 1
< 0.1%
252104007 1
< 0.1%
252004082 1
< 0.1%
252004064 1
< 0.1%
Distinct1865
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
Minimum2020-01-01 00:00:00
Maximum2025-02-11 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-07T14:18:51.633740image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-07T14:18:51.693185image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1859
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
Minimum2020-01-01 00:00:00
Maximum2025-02-10 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-07T14:18:51.749624image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-07T14:18:51.805738image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

TIME OCC
Real number (ℝ)

Distinct1439
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1339.9127
Minimum1
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2025-03-07T14:18:51.863418image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile100
Q1900
median1420
Q31900
95-th percentile2235
Maximum2359
Range2358
Interquartile range (IQR)1000

Descriptive statistics

Standard deviation651.0712
Coefficient of variation (CV)0.4859057
Kurtosis-0.78061167
Mean1339.9127
Median Absolute Deviation (MAD)480
Skewness-0.4357047
Sum1.3466497 × 109
Variance423893.7
MonotonicityNot monotonic
2025-03-07T14:18:51.914800image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1200 35200
 
3.5%
1800 26575
 
2.6%
1700 25191
 
2.5%
2000 24778
 
2.5%
1900 23080
 
2.3%
2200 22876
 
2.3%
2100 21878
 
2.2%
1600 20826
 
2.1%
1500 20225
 
2.0%
1400 17802
 
1.8%
Other values (1429) 766597
76.3%
ValueCountFrequency (%)
1 17361
1.7%
2 186
 
< 0.1%
3 185
 
< 0.1%
4 162
 
< 0.1%
5 3176
 
0.3%
6 139
 
< 0.1%
7 111
 
< 0.1%
8 107
 
< 0.1%
9 97
 
< 0.1%
10 1829
 
0.2%
ValueCountFrequency (%)
2359 774
 
0.1%
2358 85
 
< 0.1%
2357 72
 
< 0.1%
2356 53
 
< 0.1%
2355 1234
0.1%
2354 38
 
< 0.1%
2353 59
 
< 0.1%
2352 51
 
< 0.1%
2351 46
 
< 0.1%
2350 1969
0.2%

AREA
Real number (ℝ)

High correlation 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.691115
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2025-03-07T14:18:51.959784image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median11
Q316
95-th percentile20
Maximum21
Range20
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.1102604
Coefficient of variation (CV)0.57152695
Kurtosis-1.1908805
Mean10.691115
Median Absolute Deviation (MAD)5
Skewness0.012130714
Sum10744870
Variance37.335282
MonotonicityNot monotonic
2025-03-07T14:18:52.006846image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 69673
 
6.9%
12 61756
 
6.1%
14 59514
 
5.9%
3 57470
 
5.7%
6 52432
 
5.2%
15 51109
 
5.1%
20 50063
 
5.0%
18 49933
 
5.0%
13 49179
 
4.9%
7 48239
 
4.8%
Other values (11) 455660
45.3%
ValueCountFrequency (%)
1 69673
6.9%
2 46825
4.7%
3 57470
5.7%
4 37081
3.7%
5 41432
4.1%
6 52432
5.2%
7 48239
4.8%
8 45730
4.6%
9 42880
4.3%
10 42149
4.2%
ValueCountFrequency (%)
21 41368
4.1%
20 50063
5.0%
19 40349
4.0%
18 49933
5.0%
17 41754
4.2%
16 33133
3.3%
15 51109
5.1%
14 59514
5.9%
13 49179
4.9%
12 61756
6.1%

AREA NAME
Categorical

High correlation 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.6 MiB
Central
 
69673
77th Street
 
61756
Pacific
 
59514
Southwest
 
57470
Hollywood
 
52432
Other values (16)
704183 

Length

Max length11
Median length10
Mean length8.2893074
Min length6

Characters and Unicode

Total characters8330986
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWilshire
2nd rowCentral
3rd rowSouthwest
4th rowVan Nuys
5th rowHollenbeck

Common Values

ValueCountFrequency (%)
Central 69673
 
6.9%
77th Street 61756
 
6.1%
Pacific 59514
 
5.9%
Southwest 57470
 
5.7%
Hollywood 52432
 
5.2%
N Hollywood 51109
 
5.1%
Olympic 50063
 
5.0%
Southeast 49933
 
5.0%
Newton 49179
 
4.9%
Wilshire 48239
 
4.8%
Other values (11) 455660
45.3%

Length

2025-03-07T14:18:52.060865image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hollywood 103541
 
8.3%
west 87879
 
7.0%
central 69673
 
5.6%
street 61756
 
4.9%
77th 61756
 
4.9%
pacific 59514
 
4.8%
southwest 57470
 
4.6%
n 51109
 
4.1%
olympic 50063
 
4.0%
southeast 49933
 
4.0%
Other values (14) 595958
47.7%

Most occurring characters

ValueCountFrequency (%)
o 778414
 
9.3%
t 772681
 
9.3%
e 728663
 
8.7%
l 599783
 
7.2%
a 524926
 
6.3%
s 451812
 
5.4%
i 421154
 
5.1%
r 394070
 
4.7%
h 335244
 
4.0%
n 322284
 
3.9%
Other values (29) 3001955
36.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8330986
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 778414
 
9.3%
t 772681
 
9.3%
e 728663
 
8.7%
l 599783
 
7.2%
a 524926
 
6.3%
s 451812
 
5.4%
i 421154
 
5.1%
r 394070
 
4.7%
h 335244
 
4.0%
n 322284
 
3.9%
Other values (29) 3001955
36.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8330986
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 778414
 
9.3%
t 772681
 
9.3%
e 728663
 
8.7%
l 599783
 
7.2%
a 524926
 
6.3%
s 451812
 
5.4%
i 421154
 
5.1%
r 394070
 
4.7%
h 335244
 
4.0%
n 322284
 
3.9%
Other values (29) 3001955
36.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8330986
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 778414
 
9.3%
t 772681
 
9.3%
e 728663
 
8.7%
l 599783
 
7.2%
a 524926
 
6.3%
s 451812
 
5.4%
i 421154
 
5.1%
r 394070
 
4.7%
h 335244
 
4.0%
n 322284
 
3.9%
Other values (29) 3001955
36.0%

Rpt Dist No
Real number (ℝ)

High correlation 

Distinct1210
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1115.5709
Minimum101
Maximum2199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2025-03-07T14:18:52.108755image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile163
Q1587
median1139
Q31613
95-th percentile2069
Maximum2199
Range2098
Interquartile range (IQR)1026

Descriptive statistics

Standard deviation611.16078
Coefficient of variation (CV)0.54784578
Kurtosis-1.1937528
Mean1115.5709
Median Absolute Deviation (MAD)507
Skewness0.018655432
Sum1.1211799 × 109
Variance373517.5
MonotonicityNot monotonic
2025-03-07T14:18:52.234446image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
162 5403
 
0.5%
1494 5368
 
0.5%
645 5025
 
0.5%
182 4896
 
0.5%
646 4426
 
0.4%
2156 4144
 
0.4%
636 3953
 
0.4%
111 3856
 
0.4%
119 3677
 
0.4%
152 3278
 
0.3%
Other values (1200) 961002
95.6%
ValueCountFrequency (%)
101 943
 
0.1%
105 292
 
< 0.1%
109 26
 
< 0.1%
111 3856
0.4%
112 232
 
< 0.1%
118 1143
 
0.1%
119 3677
0.4%
121 475
 
< 0.1%
122 340
 
< 0.1%
123 365
 
< 0.1%
ValueCountFrequency (%)
2199 2
 
< 0.1%
2198 37
 
< 0.1%
2197 185
 
< 0.1%
2196 476
 
< 0.1%
2189 1978
0.2%
2187 1483
0.1%
2185 793
0.1%
2183 735
 
0.1%
2177 1505
0.1%
2175 1181
0.1%

Part 1-2
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.6 MiB
1
602728 
2
402300 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1005028
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 602728
60.0%
2 402300
40.0%

Length

2025-03-07T14:18:52.280112image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-07T14:18:52.315026image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1 602728
60.0%
2 402300
40.0%

Most occurring characters

ValueCountFrequency (%)
1 602728
60.0%
2 402300
40.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1005028
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 602728
60.0%
2 402300
40.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1005028
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 602728
60.0%
2 402300
40.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1005028
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 602728
60.0%
2 402300
40.0%

Crm Cd
Real number (ℝ)

High correlation 

Distinct140
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500.13675
Minimum110
Maximum956
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2025-03-07T14:18:52.356492image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile230
Q1331
median442
Q3626
95-th percentile901
Maximum956
Range846
Interquartile range (IQR)295

Descriptive statistics

Standard deviation205.25982
Coefficient of variation (CV)0.4104074
Kurtosis-0.68237159
Mean500.13675
Median Absolute Deviation (MAD)132
Skewness0.52717486
Sum5.0265144 × 108
Variance42131.595
MonotonicityNot monotonic
2025-03-07T14:18:52.406569image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
510 115211
 
11.5%
624 74832
 
7.4%
330 63518
 
6.3%
354 62538
 
6.2%
740 61089
 
6.1%
310 57873
 
5.8%
440 53723
 
5.3%
230 53530
 
5.3%
626 46710
 
4.6%
420 41312
 
4.1%
Other values (130) 374692
37.3%
ValueCountFrequency (%)
110 1567
 
0.2%
113 9
 
< 0.1%
121 3753
 
0.4%
122 318
 
< 0.1%
210 32317
3.2%
220 4837
 
0.5%
230 53530
5.3%
231 1078
 
0.1%
235 614
 
0.1%
236 12656
 
1.3%
ValueCountFrequency (%)
956 8713
0.9%
954 45
 
< 0.1%
951 337
 
< 0.1%
950 78
 
< 0.1%
949 95
 
< 0.1%
948 7
 
< 0.1%
946 6960
0.7%
944 24
 
< 0.1%
943 269
 
< 0.1%
942 8
 
< 0.1%
Distinct140
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.7 MiB
2025-03-07T14:18:52.583312image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length56
Median length47
Mean length29.272191
Min length5

Characters and Unicode

Total characters29419372
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowVEHICLE - STOLEN
2nd rowBURGLARY FROM VEHICLE
3rd rowBIKE - STOLEN
4th rowSHOPLIFTING-GRAND THEFT ($950.01 & OVER)
5th rowVEHICLE - STOLEN
ValueCountFrequency (%)
787890
 
17.1%
vehicle 266633
 
5.8%
assault 250754
 
5.5%
theft 249487
 
5.4%
under 154770
 
3.4%
from 143447
 
3.1%
stolen 133751
 
2.9%
petty 128187
 
2.8%
simple 127665
 
2.8%
950 126558
 
2.8%
Other values (239) 2228352
48.5%
2025-03-07T14:18:52.811594image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3609892
 
12.3%
E 2561399
 
8.7%
T 2261360
 
7.7%
A 1977944
 
6.7%
L 1633255
 
5.6%
R 1608263
 
5.5%
S 1332124
 
4.5%
I 1297851
 
4.4%
N 1260923
 
4.3%
O 1227887
 
4.2%
Other values (32) 10648474
36.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29419372
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3609892
 
12.3%
E 2561399
 
8.7%
T 2261360
 
7.7%
A 1977944
 
6.7%
L 1633255
 
5.6%
R 1608263
 
5.5%
S 1332124
 
4.5%
I 1297851
 
4.4%
N 1260923
 
4.3%
O 1227887
 
4.2%
Other values (32) 10648474
36.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29419372
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3609892
 
12.3%
E 2561399
 
8.7%
T 2261360
 
7.7%
A 1977944
 
6.7%
L 1633255
 
5.6%
R 1608263
 
5.5%
S 1332124
 
4.5%
I 1297851
 
4.4%
N 1260923
 
4.3%
O 1227887
 
4.2%
Other values (32) 10648474
36.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29419372
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3609892
 
12.3%
E 2561399
 
8.7%
T 2261360
 
7.7%
A 1977944
 
6.7%
L 1633255
 
5.6%
R 1608263
 
5.5%
S 1332124
 
4.5%
I 1297851
 
4.4%
N 1260923
 
4.3%
O 1227887
 
4.2%
Other values (32) 10648474
36.2%

Mocodes
Text

Missing 

Distinct310837
Distinct (%)36.4%
Missing151684
Missing (%)15.1%
Memory size65.5 MiB
2025-03-07T14:18:52.988962image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length49
Median length39
Mean length16.345818
Min length4

Characters and Unicode

Total characters13948606
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique272283 ?
Unique (%)31.9%

Sample

1st row1822 1402 0344
2nd row0344 1251
3rd row0325 1501
4th row0329 1402 2004 1501
5th row0344 1607
ValueCountFrequency (%)
1822 341736
 
11.5%
0344 298952
 
10.1%
0913 151877
 
5.1%
0329 130005
 
4.4%
0416 121103
 
4.1%
1300 97388
 
3.3%
0400 74014
 
2.5%
2000 73725
 
2.5%
1402 58608
 
2.0%
2004 51801
 
1.7%
Other values (729) 1561181
52.7%
2025-03-07T14:18:53.211722image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3130827
22.4%
2107046
15.1%
1 1846135
13.2%
2 1704076
12.2%
4 1606059
11.5%
3 1387030
9.9%
8 613513
 
4.4%
9 605125
 
4.3%
6 425062
 
3.0%
5 336568
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13948606
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3130827
22.4%
2107046
15.1%
1 1846135
13.2%
2 1704076
12.2%
4 1606059
11.5%
3 1387030
9.9%
8 613513
 
4.4%
9 605125
 
4.3%
6 425062
 
3.0%
5 336568
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13948606
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3130827
22.4%
2107046
15.1%
1 1846135
13.2%
2 1704076
12.2%
4 1606059
11.5%
3 1387030
9.9%
8 613513
 
4.4%
9 605125
 
4.3%
6 425062
 
3.0%
5 336568
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13948606
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3130827
22.4%
2107046
15.1%
1 1846135
13.2%
2 1704076
12.2%
4 1606059
11.5%
3 1387030
9.9%
8 613513
 
4.4%
9 605125
 
4.3%
6 425062
 
3.0%
5 336568
 
2.4%

Vict Age
Real number (ℝ)

Zeros 

Distinct104
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.916119
Minimum-4
Maximum120
Zeros269258
Zeros (%)26.8%
Negative135
Negative (%)< 0.1%
Memory size15.3 MiB
2025-03-07T14:18:53.274347image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile0
Q10
median30
Q344
95-th percentile65
Maximum120
Range124
Interquartile range (IQR)44

Descriptive statistics

Standard deviation21.99301
Coefficient of variation (CV)0.7605796
Kurtosis-0.79256073
Mean28.916119
Median Absolute Deviation (MAD)17
Skewness0.1625431
Sum29061509
Variance483.69249
MonotonicityNot monotonic
2025-03-07T14:18:53.328560image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 269258
26.8%
30 22287
 
2.2%
35 21829
 
2.2%
31 21420
 
2.1%
29 21344
 
2.1%
28 20969
 
2.1%
32 20844
 
2.1%
33 20362
 
2.0%
27 20131
 
2.0%
34 19420
 
1.9%
Other values (94) 547164
54.4%
ValueCountFrequency (%)
-4 3
 
< 0.1%
-3 5
 
< 0.1%
-2 28
 
< 0.1%
-1 99
 
< 0.1%
0 269258
26.8%
2 429
 
< 0.1%
3 501
 
< 0.1%
4 520
 
0.1%
5 572
 
0.1%
6 563
 
0.1%
ValueCountFrequency (%)
120 1
 
< 0.1%
99 354
< 0.1%
98 71
 
< 0.1%
97 72
 
< 0.1%
96 95
 
< 0.1%
95 100
 
< 0.1%
94 105
 
< 0.1%
93 124
 
< 0.1%
92 175
< 0.1%
91 231
< 0.1%

Vict Sex
Categorical

High correlation  Missing 

Distinct5
Distinct (%)< 0.1%
Missing144694
Missing (%)14.4%
Memory size56.4 MiB
M
403878 
F
358578 
X
97763 
H
 
114
-
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters860334
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowM
2nd rowM
3rd rowX
4th rowM
5th rowX

Common Values

ValueCountFrequency (%)
M 403878
40.2%
F 358578
35.7%
X 97763
 
9.7%
H 114
 
< 0.1%
- 1
 
< 0.1%
(Missing) 144694
 
14.4%

Length

2025-03-07T14:18:53.375055image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-07T14:18:53.415503image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
m 403878
46.9%
f 358578
41.7%
x 97763
 
11.4%
h 114
 
< 0.1%
1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
M 403878
46.9%
F 358578
41.7%
X 97763
 
11.4%
H 114
 
< 0.1%
- 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 860334
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 403878
46.9%
F 358578
41.7%
X 97763
 
11.4%
H 114
 
< 0.1%
- 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 860334
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 403878
46.9%
F 358578
41.7%
X 97763
 
11.4%
H 114
 
< 0.1%
- 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 860334
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 403878
46.9%
F 358578
41.7%
X 97763
 
11.4%
H 114
 
< 0.1%
- 1
 
< 0.1%

Vict Descent
Categorical

High correlation  Missing 

Distinct20
Distinct (%)< 0.1%
Missing144706
Missing (%)14.4%
Memory size56.4 MiB
H
296407 
W
201442 
B
135817 
X
106671 
O
78001 
Other values (15)
41984 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters860322
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowO
2nd rowO
3rd rowX
4th rowO
5th rowX

Common Values

ValueCountFrequency (%)
H 296407
29.5%
W 201442
20.0%
B 135817
13.5%
X 106671
 
10.6%
O 78001
 
7.8%
A 21340
 
2.1%
K 5990
 
0.6%
F 4840
 
0.5%
C 4631
 
0.5%
J 1586
 
0.2%
Other values (10) 3597
 
0.4%
(Missing) 144706
14.4%

Length

2025-03-07T14:18:53.461028image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
h 296407
34.5%
w 201442
23.4%
b 135817
15.8%
x 106671
 
12.4%
o 78001
 
9.1%
a 21340
 
2.5%
k 5990
 
0.7%
f 4840
 
0.6%
c 4631
 
0.5%
j 1586
 
0.2%
Other values (10) 3597
 
0.4%

Most occurring characters

ValueCountFrequency (%)
H 296407
34.5%
W 201442
23.4%
B 135817
15.8%
X 106671
 
12.4%
O 78001
 
9.1%
A 21340
 
2.5%
K 5990
 
0.7%
F 4840
 
0.6%
C 4631
 
0.5%
J 1586
 
0.2%
Other values (10) 3597
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 860322
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
H 296407
34.5%
W 201442
23.4%
B 135817
15.8%
X 106671
 
12.4%
O 78001
 
9.1%
A 21340
 
2.5%
K 5990
 
0.7%
F 4840
 
0.6%
C 4631
 
0.5%
J 1586
 
0.2%
Other values (10) 3597
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 860322
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
H 296407
34.5%
W 201442
23.4%
B 135817
15.8%
X 106671
 
12.4%
O 78001
 
9.1%
A 21340
 
2.5%
K 5990
 
0.7%
F 4840
 
0.6%
C 4631
 
0.5%
J 1586
 
0.2%
Other values (10) 3597
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 860322
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
H 296407
34.5%
W 201442
23.4%
B 135817
15.8%
X 106671
 
12.4%
O 78001
 
9.1%
A 21340
 
2.5%
K 5990
 
0.7%
F 4840
 
0.6%
C 4631
 
0.5%
J 1586
 
0.2%
Other values (10) 3597
 
0.4%

Premis Cd
Real number (ℝ)

Distinct314
Distinct (%)< 0.1%
Missing16
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean305.6078
Minimum101
Maximum976
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2025-03-07T14:18:53.506354image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1101
median203
Q3501
95-th percentile708
Maximum976
Range875
Interquartile range (IQR)400

Descriptive statistics

Standard deviation219.3027
Coefficient of variation (CV)0.71759522
Kurtosis-0.90192351
Mean305.6078
Median Absolute Deviation (MAD)102
Skewness0.57941019
Sum3.0713951 × 108
Variance48093.673
MonotonicityNot monotonic
2025-03-07T14:18:53.558169image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101 261332
26.0%
501 163649
16.3%
502 119007
11.8%
108 69151
 
6.9%
203 47645
 
4.7%
102 40862
 
4.1%
122 29302
 
2.9%
707 19363
 
1.9%
104 16083
 
1.6%
404 14431
 
1.4%
Other values (304) 224187
22.3%
ValueCountFrequency (%)
101 261332
26.0%
102 40862
 
4.1%
103 7066
 
0.7%
104 16083
 
1.6%
105 23
 
< 0.1%
106 41
 
< 0.1%
107 303
 
< 0.1%
108 69151
 
6.9%
109 6665
 
0.7%
110 347
 
< 0.1%
ValueCountFrequency (%)
976 2
 
< 0.1%
975 1
 
< 0.1%
974 11
 
< 0.1%
973 7
 
< 0.1%
972 15
 
< 0.1%
971 56
< 0.1%
970 57
< 0.1%
969 40
 
< 0.1%
968 46
< 0.1%
967 114
< 0.1%
Distinct306
Distinct (%)< 0.1%
Missing588
Missing (%)0.1%
Memory size71.4 MiB
2025-03-07T14:18:53.684600image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length63
Median length56
Mean length17.519988
Min length4

Characters and Unicode

Total characters17597777
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSTREET
2nd rowBUS STOP/LAYOVER (ALSO QUERY 124)
3rd rowMULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)
4th rowCLOTHING STORE
5th rowSTREET
ValueCountFrequency (%)
dwelling 282656
 
12.5%
street 261334
 
11.5%
single 164070
 
7.2%
family 163649
 
7.2%
etc 121525
 
5.4%
multi-unit 119007
 
5.3%
apartment 119007
 
5.3%
duplex 119007
 
5.3%
parking 78027
 
3.4%
lot 70499
 
3.1%
Other values (491) 766654
33.8%
2025-03-07T14:18:53.876193image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 2039829
 
11.6%
T 1638846
 
9.3%
L 1450014
 
8.2%
I 1302484
 
7.4%
1260995
 
7.2%
N 1084030
 
6.2%
R 964581
 
5.5%
S 911648
 
5.2%
A 899522
 
5.1%
G 675486
 
3.8%
Other values (35) 5370342
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17597777
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 2039829
 
11.6%
T 1638846
 
9.3%
L 1450014
 
8.2%
I 1302484
 
7.4%
1260995
 
7.2%
N 1084030
 
6.2%
R 964581
 
5.5%
S 911648
 
5.2%
A 899522
 
5.1%
G 675486
 
3.8%
Other values (35) 5370342
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17597777
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 2039829
 
11.6%
T 1638846
 
9.3%
L 1450014
 
8.2%
I 1302484
 
7.4%
1260995
 
7.2%
N 1084030
 
6.2%
R 964581
 
5.5%
S 911648
 
5.2%
A 899522
 
5.1%
G 675486
 
3.8%
Other values (35) 5370342
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17597777
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 2039829
 
11.6%
T 1638846
 
9.3%
L 1450014
 
8.2%
I 1302484
 
7.4%
1260995
 
7.2%
N 1084030
 
6.2%
R 964581
 
5.5%
S 911648
 
5.2%
A 899522
 
5.1%
G 675486
 
3.8%
Other values (35) 5370342
30.5%

Weapon Used Cd
Real number (ℝ)

High correlation  Missing 

Distinct79
Distinct (%)< 0.1%
Missing677800
Missing (%)67.4%
Infinite0
Infinite (%)0.0%
Mean363.9465
Minimum101
Maximum516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2025-03-07T14:18:53.933801image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile102
Q1311
median400
Q3400
95-th percentile511
Maximum516
Range415
Interquartile range (IQR)89

Descriptive statistics

Standard deviation123.74035
Coefficient of variation (CV)0.33999599
Kurtosis-0.046407546
Mean363.9465
Median Absolute Deviation (MAD)0
Skewness-0.99369586
Sum1.1909348 × 108
Variance15311.674
MonotonicityNot monotonic
2025-03-07T14:18:53.985296image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400 174742
 
17.4%
500 36386
 
3.6%
511 23844
 
2.4%
102 20185
 
2.0%
109 7267
 
0.7%
200 6838
 
0.7%
106 6581
 
0.7%
207 5881
 
0.6%
512 3730
 
0.4%
307 3260
 
0.3%
Other values (69) 38514
 
3.8%
(Missing) 677800
67.4%
ValueCountFrequency (%)
101 1186
 
0.1%
102 20185
2.0%
103 497
 
< 0.1%
104 316
 
< 0.1%
105 29
 
< 0.1%
106 6581
 
0.7%
107 948
 
0.1%
108 28
 
< 0.1%
109 7267
 
0.7%
110 55
 
< 0.1%
ValueCountFrequency (%)
516 53
 
< 0.1%
515 945
 
0.1%
514 149
 
< 0.1%
513 395
 
< 0.1%
512 3730
 
0.4%
511 23844
2.4%
510 145
 
< 0.1%
509 50
 
< 0.1%
508 14
 
< 0.1%
507 54
 
< 0.1%

Weapon Desc
Text

Missing 

Distinct79
Distinct (%)< 0.1%
Missing677800
Missing (%)67.4%
Memory size53.8 MiB
2025-03-07T14:18:54.151666image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length46
Median length46
Mean length32.621576
Min length3

Characters and Unicode

Total characters10674693
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSTRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)
2nd rowVERBAL THREAT
3rd rowKNIFE WITH BLADE OVER 6 INCHES IN LENGTH
4th rowSTRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)
5th rowUNKNOWN WEAPON/OTHER WEAPON
ValueCountFrequency (%)
or 181580
11.4%
strong-arm 174742
11.0%
force 174742
11.0%
bodily 174742
11.0%
hands 174742
11.0%
feet 174742
11.0%
fist 174742
11.0%
unknown 43851
 
2.8%
weapon 36391
 
2.3%
weapon/other 36386
 
2.3%
Other values (117) 243425
15.3%
2025-03-07T14:18:54.370731image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1262857
 
11.8%
O 906087
 
8.5%
R 851605
 
8.0%
E 811278
 
7.6%
T 689851
 
6.5%
N 651379
 
6.1%
S 583241
 
5.5%
F 557091
 
5.2%
A 545039
 
5.1%
I 453422
 
4.2%
Other values (30) 3362843
31.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10674693
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1262857
 
11.8%
O 906087
 
8.5%
R 851605
 
8.0%
E 811278
 
7.6%
T 689851
 
6.5%
N 651379
 
6.1%
S 583241
 
5.5%
F 557091
 
5.2%
A 545039
 
5.1%
I 453422
 
4.2%
Other values (30) 3362843
31.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10674693
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1262857
 
11.8%
O 906087
 
8.5%
R 851605
 
8.0%
E 811278
 
7.6%
T 689851
 
6.5%
N 651379
 
6.1%
S 583241
 
5.5%
F 557091
 
5.2%
A 545039
 
5.1%
I 453422
 
4.2%
Other values (30) 3362843
31.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10674693
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1262857
 
11.8%
O 906087
 
8.5%
R 851605
 
8.0%
E 811278
 
7.6%
T 689851
 
6.5%
N 651379
 
6.1%
S 583241
 
5.5%
F 557091
 
5.2%
A 545039
 
5.1%
I 453422
 
4.2%
Other values (30) 3362843
31.5%

Status
Categorical

High correlation  Imbalance 

Distinct6
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size56.5 MiB
IC
804396 
AO
108883 
AA
86639 
JA
 
3245
JO
 
1858

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2010054
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAA
2nd rowIC
3rd rowIC
4th rowIC
5th rowIC

Common Values

ValueCountFrequency (%)
IC 804396
80.0%
AO 108883
 
10.8%
AA 86639
 
8.6%
JA 3245
 
0.3%
JO 1858
 
0.2%
CC 6
 
< 0.1%
(Missing) 1
 
< 0.1%

Length

2025-03-07T14:18:54.424608image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-07T14:18:54.465931image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
ic 804396
80.0%
ao 108883
 
10.8%
aa 86639
 
8.6%
ja 3245
 
0.3%
jo 1858
 
0.2%
cc 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
C 804408
40.0%
I 804396
40.0%
A 285406
 
14.2%
O 110741
 
5.5%
J 5103
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2010054
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 804408
40.0%
I 804396
40.0%
A 285406
 
14.2%
O 110741
 
5.5%
J 5103
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2010054
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 804408
40.0%
I 804396
40.0%
A 285406
 
14.2%
O 110741
 
5.5%
J 5103
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2010054
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 804408
40.0%
I 804396
40.0%
A 285406
 
14.2%
O 110741
 
5.5%
J 5103
 
0.3%

Status Desc
Categorical

High correlation  Imbalance 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.3 MiB
Invest Cont
804396 
Adult Other
108883 
Adult Arrest
86639 
Juv Arrest
 
3245
Juv Other
 
1858

Length

Max length12
Median length11
Mean length11.079224
Min length3

Characters and Unicode

Total characters11134930
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAdult Arrest
2nd rowInvest Cont
3rd rowInvest Cont
4th rowInvest Cont
5th rowInvest Cont

Common Values

ValueCountFrequency (%)
Invest Cont 804396
80.0%
Adult Other 108883
 
10.8%
Adult Arrest 86639
 
8.6%
Juv Arrest 3245
 
0.3%
Juv Other 1858
 
0.2%
UNK 7
 
< 0.1%

Length

2025-03-07T14:18:54.510784image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-07T14:18:54.551342image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
invest 804396
40.0%
cont 804396
40.0%
adult 195522
 
9.7%
other 110741
 
5.5%
arrest 89884
 
4.5%
juv 5103
 
0.3%
unk 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
t 2004939
18.0%
n 1608792
14.4%
e 1005021
9.0%
1005021
9.0%
s 894280
8.0%
v 809499
7.3%
I 804396
7.2%
C 804396
7.2%
o 804396
7.2%
r 290509
 
2.6%
Other values (10) 1103681
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11134930
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 2004939
18.0%
n 1608792
14.4%
e 1005021
9.0%
1005021
9.0%
s 894280
8.0%
v 809499
7.3%
I 804396
7.2%
C 804396
7.2%
o 804396
7.2%
r 290509
 
2.6%
Other values (10) 1103681
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11134930
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 2004939
18.0%
n 1608792
14.4%
e 1005021
9.0%
1005021
9.0%
s 894280
8.0%
v 809499
7.3%
I 804396
7.2%
C 804396
7.2%
o 804396
7.2%
r 290509
 
2.6%
Other values (10) 1103681
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11134930
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 2004939
18.0%
n 1608792
14.4%
e 1005021
9.0%
1005021
9.0%
s 894280
8.0%
v 809499
7.3%
I 804396
7.2%
C 804396
7.2%
o 804396
7.2%
r 290509
 
2.6%
Other values (10) 1103681
9.9%

Crm Cd 1
Real number (ℝ)

High correlation 

Distinct142
Distinct (%)< 0.1%
Missing11
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean499.89732
Minimum110
Maximum956
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2025-03-07T14:18:54.601980image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile230
Q1331
median442
Q3626
95-th percentile901
Maximum956
Range846
Interquartile range (IQR)295

Descriptive statistics

Standard deviation205.06036
Coefficient of variation (CV)0.41020495
Kurtosis-0.67756934
Mean499.89732
Median Absolute Deviation (MAD)132
Skewness0.52827484
Sum5.024053 × 108
Variance42049.75
MonotonicityNot monotonic
2025-03-07T14:18:54.719804image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
510 115195
 
11.5%
624 75167
 
7.5%
330 63570
 
6.3%
354 62551
 
6.2%
740 61193
 
6.1%
310 57868
 
5.8%
440 53724
 
5.3%
230 53537
 
5.3%
626 47018
 
4.7%
420 41319
 
4.1%
Other values (132) 373875
37.2%
ValueCountFrequency (%)
110 1567
 
0.2%
113 9
 
< 0.1%
121 3753
 
0.4%
122 318
 
< 0.1%
210 32324
3.2%
220 4837
 
0.5%
230 53537
5.3%
231 1078
 
0.1%
235 615
 
0.1%
236 12660
 
1.3%
ValueCountFrequency (%)
956 8713
0.9%
954 45
 
< 0.1%
951 317
 
< 0.1%
950 77
 
< 0.1%
949 95
 
< 0.1%
948 7
 
< 0.1%
946 6960
0.7%
944 24
 
< 0.1%
943 269
 
< 0.1%
942 8
 
< 0.1%

Crm Cd 2
Real number (ℝ)

Missing 

Distinct126
Distinct (%)0.2%
Missing935892
Missing (%)93.1%
Infinite0
Infinite (%)0.0%
Mean958.11639
Minimum210
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2025-03-07T14:18:54.771002image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum210
5-th percentile740
Q1998
median998
Q3998
95-th percentile998
Maximum999
Range789
Interquartile range (IQR)0

Descriptive statistics

Standard deviation110.35685
Coefficient of variation (CV)0.11518105
Kurtosis15.188602
Mean958.11639
Median Absolute Deviation (MAD)0
Skewness-3.6886503
Sum66240335
Variance12178.635
MonotonicityNot monotonic
2025-03-07T14:18:54.826632image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
998 55309
 
5.5%
930 3866
 
0.4%
860 1362
 
0.1%
761 977
 
0.1%
946 898
 
0.1%
626 835
 
0.1%
812 660
 
0.1%
740 607
 
0.1%
910 481
 
< 0.1%
480 421
 
< 0.1%
Other values (116) 3720
 
0.4%
(Missing) 935892
93.1%
ValueCountFrequency (%)
210 19
 
< 0.1%
220 15
 
< 0.1%
230 118
< 0.1%
231 4
 
< 0.1%
235 2
 
< 0.1%
236 102
< 0.1%
250 26
 
< 0.1%
251 11
 
< 0.1%
310 50
< 0.1%
320 3
 
< 0.1%
ValueCountFrequency (%)
999 151
 
< 0.1%
998 55309
5.5%
997 31
 
< 0.1%
996 5
 
< 0.1%
994 7
 
< 0.1%
993 16
 
< 0.1%
990 18
 
< 0.1%
980 32
 
< 0.1%
979 3
 
< 0.1%
978 3
 
< 0.1%

Crm Cd 3
Real number (ℝ)

Missing 

Distinct38
Distinct (%)1.6%
Missing1002713
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean984.02203
Minimum310
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2025-03-07T14:18:54.879035image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum310
5-th percentile910
Q1998
median998
Q3998
95-th percentile998
Maximum999
Range689
Interquartile range (IQR)0

Descriptive statistics

Standard deviation52.340476
Coefficient of variation (CV)0.05319035
Kurtosis35.708585
Mean984.02203
Median Absolute Deviation (MAD)0
Skewness-5.3127605
Sum2278011
Variance2739.5254
MonotonicityNot monotonic
2025-03-07T14:18:54.926994image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
998 2043
 
0.2%
930 85
 
< 0.1%
946 31
 
< 0.1%
910 27
 
< 0.1%
761 23
 
< 0.1%
999 18
 
< 0.1%
740 13
 
< 0.1%
860 7
 
< 0.1%
626 6
 
< 0.1%
901 6
 
< 0.1%
Other values (28) 56
 
< 0.1%
(Missing) 1002713
99.8%
ValueCountFrequency (%)
310 1
 
< 0.1%
434 1
 
< 0.1%
520 1
 
< 0.1%
626 6
< 0.1%
648 1
 
< 0.1%
649 1
 
< 0.1%
653 5
 
< 0.1%
740 13
< 0.1%
745 5
 
< 0.1%
753 2
 
< 0.1%
ValueCountFrequency (%)
999 18
 
< 0.1%
998 2043
0.2%
997 3
 
< 0.1%
993 3
 
< 0.1%
990 1
 
< 0.1%
956 5
 
< 0.1%
946 31
 
< 0.1%
943 1
 
< 0.1%
940 1
 
< 0.1%
933 1
 
< 0.1%

Crm Cd 4
Real number (ℝ)

Missing 

Distinct6
Distinct (%)9.4%
Missing1004964
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean991.21875
Minimum821
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2025-03-07T14:18:54.968521image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum821
5-th percentile946
Q1998
median998
Q3998
95-th percentile998
Maximum999
Range178
Interquartile range (IQR)0

Descriptive statistics

Standard deviation27.06985
Coefficient of variation (CV)0.027309663
Kurtosis26.296475
Mean991.21875
Median Absolute Deviation (MAD)0
Skewness-4.8431364
Sum63438
Variance732.77679
MonotonicityNot monotonic
2025-03-07T14:18:55.007236image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
998 56
 
< 0.1%
999 3
 
< 0.1%
946 2
 
< 0.1%
930 1
 
< 0.1%
821 1
 
< 0.1%
910 1
 
< 0.1%
(Missing) 1004964
> 99.9%
ValueCountFrequency (%)
821 1
 
< 0.1%
910 1
 
< 0.1%
930 1
 
< 0.1%
946 2
 
< 0.1%
998 56
< 0.1%
999 3
 
< 0.1%
ValueCountFrequency (%)
999 3
 
< 0.1%
998 56
< 0.1%
946 2
 
< 0.1%
930 1
 
< 0.1%
910 1
 
< 0.1%
821 1
 
< 0.1%
Distinct66565
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size89.1 MiB
2025-03-07T14:18:55.144523image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length40
Median length39
Mean length35.93788
Min length1

Characters and Unicode

Total characters36118576
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13281 ?
Unique (%)1.3%

Sample

1st row1900 S LONGWOOD AV
2nd row1000 S FLOWER ST
3rd row1400 W 37TH ST
4th row14000 RIVERSIDE DR
5th row200 E AVENUE 28
ValueCountFrequency (%)
st 339324
 
10.3%
av 288231
 
8.8%
bl 192978
 
5.9%
s 136512
 
4.2%
w 116453
 
3.6%
n 60858
 
1.9%
e 51883
 
1.6%
dr 38594
 
1.2%
pl 28417
 
0.9%
600 26164
 
0.8%
Other values (7790) 1999550
61.0%
2025-03-07T14:18:55.355364image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
23604456
65.4%
0 1831562
 
5.1%
A 985907
 
2.7%
S 807365
 
2.2%
T 770254
 
2.1%
E 708774
 
2.0%
N 642213
 
1.8%
L 638718
 
1.8%
R 571885
 
1.6%
O 534626
 
1.5%
Other values (27) 5022816
 
13.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36118576
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
23604456
65.4%
0 1831562
 
5.1%
A 985907
 
2.7%
S 807365
 
2.2%
T 770254
 
2.1%
E 708774
 
2.0%
N 642213
 
1.8%
L 638718
 
1.8%
R 571885
 
1.6%
O 534626
 
1.5%
Other values (27) 5022816
 
13.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36118576
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
23604456
65.4%
0 1831562
 
5.1%
A 985907
 
2.7%
S 807365
 
2.2%
T 770254
 
2.1%
E 708774
 
2.0%
N 642213
 
1.8%
L 638718
 
1.8%
R 571885
 
1.6%
O 534626
 
1.5%
Other values (27) 5022816
 
13.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36118576
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
23604456
65.4%
0 1831562
 
5.1%
A 985907
 
2.7%
S 807365
 
2.2%
T 770254
 
2.1%
E 708774
 
2.0%
N 642213
 
1.8%
L 638718
 
1.8%
R 571885
 
1.6%
O 534626
 
1.5%
Other values (27) 5022816
 
13.9%

Cross Street
Text

Missing 

Distinct10413
Distinct (%)6.8%
Missing850799
Missing (%)84.7%
Memory size43.9 MiB
2025-03-07T14:18:55.482794image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length34
Median length31
Mean length20.824047
Min length1

Characters and Unicode

Total characters3211672
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4054 ?
Unique (%)2.6%

Sample

1st rowALVARADO
2nd rowFOUNTAIN
3rd rowWADSWORTH
4th rowCROCKER
5th rowRANCHITO
ValueCountFrequency (%)
st 37246
 
13.9%
av 27588
 
10.3%
bl 14609
 
5.5%
figueroa 3870
 
1.4%
san 3649
 
1.4%
vermont 2972
 
1.1%
broadway 2808
 
1.0%
western 2610
 
1.0%
s 2469
 
0.9%
pl 2424
 
0.9%
Other values (4327) 167715
62.6%
2025-03-07T14:18:55.662572image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1976211
61.5%
A 135960
 
4.2%
E 105441
 
3.3%
T 96634
 
3.0%
N 93039
 
2.9%
S 90477
 
2.8%
O 86975
 
2.7%
R 85914
 
2.7%
L 78136
 
2.4%
I 55964
 
1.7%
Other values (27) 406921
 
12.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3211672
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1976211
61.5%
A 135960
 
4.2%
E 105441
 
3.3%
T 96634
 
3.0%
N 93039
 
2.9%
S 90477
 
2.8%
O 86975
 
2.7%
R 85914
 
2.7%
L 78136
 
2.4%
I 55964
 
1.7%
Other values (27) 406921
 
12.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3211672
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1976211
61.5%
A 135960
 
4.2%
E 105441
 
3.3%
T 96634
 
3.0%
N 93039
 
2.9%
S 90477
 
2.8%
O 86975
 
2.7%
R 85914
 
2.7%
L 78136
 
2.4%
I 55964
 
1.7%
Other values (27) 406921
 
12.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3211672
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1976211
61.5%
A 135960
 
4.2%
E 105441
 
3.3%
T 96634
 
3.0%
N 93039
 
2.9%
S 90477
 
2.8%
O 86975
 
2.7%
R 85914
 
2.7%
L 78136
 
2.4%
I 55964
 
1.7%
Other values (27) 406921
 
12.7%

LAT
Real number (ℝ)

High correlation  Skewed 

Distinct5426
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.998196
Minimum0
Maximum34.3343
Zeros2240
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2025-03-07T14:18:55.720177image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33.9265
Q134.0147
median34.0589
Q334.1649
95-th percentile34.2528
Maximum34.3343
Range34.3343
Interquartile range (IQR)0.1502

Descriptive statistics

Standard deviation1.6106833
Coefficient of variation (CV)0.047375552
Kurtosis439.44151
Mean33.998196
Median Absolute Deviation (MAD)0.0631
Skewness-20.960171
Sum34169139
Variance2.5943006
MonotonicityNot monotonic
2025-03-07T14:18:55.774660image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.1016 5705
 
0.6%
34.2012 4506
 
0.4%
34.098 4266
 
0.4%
34.1939 3542
 
0.4%
34.1867 3299
 
0.3%
33.9455 3168
 
0.3%
34.0561 3060
 
0.3%
34.1649 2966
 
0.3%
34.0473 2871
 
0.3%
34.1903 2704
 
0.3%
Other values (5416) 968941
96.4%
ValueCountFrequency (%)
0 2240
0.2%
33.7059 1
 
< 0.1%
33.7061 2
 
< 0.1%
33.7064 15
 
< 0.1%
33.7065 4
 
< 0.1%
33.7068 4
 
< 0.1%
33.707 83
 
< 0.1%
33.7071 1
 
< 0.1%
33.7074 2
 
< 0.1%
33.7076 1
 
< 0.1%
ValueCountFrequency (%)
34.3343 2
< 0.1%
34.333 1
 
< 0.1%
34.3297 3
< 0.1%
34.3293 1
 
< 0.1%
34.3292 3
< 0.1%
34.3291 3
< 0.1%
34.3289 2
< 0.1%
34.3287 2
< 0.1%
34.3286 1
 
< 0.1%
34.3283 4
< 0.1%

LON
Real number (ℝ)

High correlation  Skewed 

Distinct4982
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-118.09087
Minimum-118.6676
Maximum0
Zeros2240
Zeros (%)0.2%
Negative1002788
Negative (%)99.8%
Memory size15.3 MiB
2025-03-07T14:18:55.826946image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-118.6676
5-th percentile-118.5669
Q1-118.4305
median-118.3225
Q3-118.2739
95-th percentile-118.2224
Maximum0
Range118.6676
Interquartile range (IQR)0.1566

Descriptive statistics

Standard deviation5.5822831
Coefficient of variation (CV)-0.047271079
Kurtosis443.3657
Mean-118.09087
Median Absolute Deviation (MAD)0.0639
Skewness21.099945
Sum-1.1868464 × 108
Variance31.161884
MonotonicityNot monotonic
2025-03-07T14:18:55.880950image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-118.2739 7549
 
0.8%
-118.2827 6434
 
0.6%
-118.2915 4297
 
0.4%
-118.2652 4258
 
0.4%
-118.2871 3923
 
0.4%
-118.3089 3861
 
0.4%
-118.2783 3759
 
0.4%
-118.2916 3730
 
0.4%
-118.309 3628
 
0.4%
-118.4662 2725
 
0.3%
Other values (4972) 960864
95.6%
ValueCountFrequency (%)
-118.6676 6
< 0.1%
-118.6673 12
< 0.1%
-118.6672 3
 
< 0.1%
-118.6666 1
 
< 0.1%
-118.6665 3
 
< 0.1%
-118.6663 1
 
< 0.1%
-118.6661 3
 
< 0.1%
-118.6652 8
< 0.1%
-118.6644 9
< 0.1%
-118.6642 2
 
< 0.1%
ValueCountFrequency (%)
0 2240
0.2%
-118.1554 4
 
< 0.1%
-118.156 25
 
< 0.1%
-118.1568 1
 
< 0.1%
-118.1569 1
 
< 0.1%
-118.1574 2
 
< 0.1%
-118.158 2
 
< 0.1%
-118.1581 2
 
< 0.1%
-118.1584 1
 
< 0.1%
-118.1585 10
 
< 0.1%

Interactions

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2025-03-07T14:18:43.870007image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-07T14:18:44.545330image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-07T14:18:45.146252image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-07T14:18:46.042696image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-03-07T14:18:55.924477image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
AREAAREA NAMECrm CdCrm Cd 1Crm Cd 2Crm Cd 3Crm Cd 4DR_NOLATLONPart 1-2Premis CdRpt Dist NoStatusStatus DescTIME OCCVict AgeVict DescentVict SexWeapon Used Cd
AREA1.0001.000-0.002-0.002-0.0480.0190.1120.1940.270-0.4380.037-0.0140.9990.0300.0300.0000.0260.1440.059-0.016
AREA NAME1.0001.0000.0730.0730.0780.0770.2760.1380.0240.0240.0540.1020.9090.0550.0550.0210.0590.1250.1010.090
Crm Cd-0.0020.0731.0000.999-0.1940.0520.086-0.010-0.0010.0100.8460.097-0.0020.1950.1950.005-0.0660.1210.1350.330
Crm Cd 1-0.0020.0730.9991.000-0.1680.1460.144-0.010-0.0010.0100.8460.097-0.0020.1950.1950.005-0.0660.1210.1360.331
Crm Cd 2-0.0480.078-0.194-0.1681.0000.3020.295-0.0010.0310.0320.214-0.098-0.0480.0740.0740.010-0.0220.0720.148-0.183
Crm Cd 30.0190.0770.0520.1460.3021.0000.4210.0580.006-0.0030.059-0.0040.0190.0450.045-0.014-0.0280.0000.058-0.048
Crm Cd 40.1120.2760.0860.1440.2950.4211.0000.1960.069-0.0180.000-0.1320.1540.0000.000-0.0990.0780.0000.000-0.011
DR_NO0.1940.138-0.010-0.010-0.0010.0580.1961.0000.070-0.0990.0410.0060.1940.0390.039-0.004-0.0620.0570.0490.020
LAT0.2700.024-0.001-0.0010.0310.0060.0690.0701.000-0.5380.0290.0440.2620.0230.0230.0060.0400.0090.0060.047
LON-0.4380.0240.0100.0100.032-0.003-0.018-0.099-0.5381.0000.029-0.062-0.4360.0230.023-0.004-0.0720.0090.006-0.067
Part 1-20.0370.0540.8460.8460.2140.0590.0000.0410.0290.0291.0000.3700.0300.2170.2170.1040.2550.1280.1580.548
Premis Cd-0.0140.1020.0970.097-0.098-0.004-0.1320.0060.044-0.0620.3701.000-0.0140.1010.101-0.0840.2170.1520.2280.195
Rpt Dist No0.9990.909-0.002-0.002-0.0480.0190.1540.1940.262-0.4360.030-0.0141.0000.0260.0260.0000.0260.1260.060-0.016
Status0.0300.0550.1950.1950.0740.0450.0000.0390.0230.0230.2170.1010.0261.0001.0000.0170.0810.0720.0600.082
Status Desc0.0300.0550.1950.1950.0740.0450.0000.0390.0230.0230.2170.1010.0261.0001.0000.0170.0810.0720.0600.082
TIME OCC0.0000.0210.0050.0050.010-0.014-0.099-0.0040.006-0.0040.104-0.0840.0000.0170.0171.000-0.0440.0230.027-0.019
Vict Age0.0260.059-0.066-0.066-0.022-0.0280.078-0.0620.040-0.0720.2550.2170.0260.0810.081-0.0441.0000.2700.3790.071
Vict Descent0.1440.1250.1210.1210.0720.0000.0000.0570.0090.0090.1280.1520.1260.0720.0720.0230.2701.0000.5940.080
Vict Sex0.0590.1010.1350.1360.1480.0580.0000.0490.0060.0060.1580.2280.0600.0600.0600.0270.3790.5941.0000.160
Weapon Used Cd-0.0160.0900.3300.331-0.183-0.048-0.0110.0200.047-0.0670.5480.195-0.0160.0820.082-0.0190.0710.0800.1601.000

Missing values

2025-03-07T14:18:47.328767image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-07T14:18:48.583564image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-07T14:18:50.755367image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DR_NODate RptdDATE OCCTIME OCCAREAAREA NAMERpt Dist NoPart 1-2Crm CdCrm Cd DescMocodesVict AgeVict SexVict DescentPremis CdPremis DescWeapon Used CdWeapon DescStatusStatus DescCrm Cd 1Crm Cd 2Crm Cd 3Crm Cd 4LOCATIONCross StreetLATLON
019032647503/01/2020 12:00:00 AM03/01/2020 12:00:00 AM21307Wilshire7841510VEHICLE - STOLENNaN0MO101.0STREETNaNNaNAAAdult Arrest510.0998.0NaNNaN1900 S LONGWOOD AVNaN34.0375-118.3506
120010675302/09/2020 12:00:00 AM02/08/2020 12:00:00 AM18001Central1821330BURGLARY FROM VEHICLE1822 1402 034447MO128.0BUS STOP/LAYOVER (ALSO QUERY 124)NaNNaNICInvest Cont330.0998.0NaNNaN1000 S FLOWER STNaN34.0444-118.2628
220032025811/11/2020 12:00:00 AM11/04/2020 12:00:00 AM17003Southwest3561480BIKE - STOLEN0344 125119XX502.0MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)NaNNaNICInvest Cont480.0NaNNaNNaN1400 W 37TH STNaN34.0210-118.3002
320090721705/10/2023 12:00:00 AM03/10/2020 12:00:00 AM20379Van Nuys9641343SHOPLIFTING-GRAND THEFT ($950.01 & OVER)0325 150119MO405.0CLOTHING STORENaNNaNICInvest Cont343.0NaNNaNNaN14000 RIVERSIDE DRNaN34.1576-118.4387
420041258209/09/2020 12:00:00 AM09/09/2020 12:00:00 AM6304Hollenbeck4131510VEHICLE - STOLENNaN0NaNNaN101.0STREETNaNNaNICInvest Cont510.0NaNNaNNaN200 E AVENUE 28NaN34.0820-118.2130
520020971305/03/2020 12:00:00 AM05/02/2020 12:00:00 AM18002Rampart2451510VEHICLE - STOLENNaN0NaNNaN101.0STREETNaNNaNICInvest Cont510.0NaNNaNNaN2500 W 4TH STNaN34.0642-118.2771
620020075907/07/2020 12:00:00 AM07/07/2020 12:00:00 AM13402Rampart2651648ARSON0329 1402 2004 15010XX101.0STREETNaNNaNICInvest Cont648.0998.0NaNNaNJAMES M WOODALVARADO34.0536-118.2788
720130873903/27/2020 12:00:00 AM03/27/2020 12:00:00 AM121013Newton13331510VEHICLE - STOLENNaN0NaNNaN101.0STREETNaNNaNICInvest Cont510.0NaNNaNNaN3200 S SAN PEDRO STNaN34.0170-118.2643
820111206507/31/2020 12:00:00 AM07/30/2020 12:00:00 AM203011Northeast11611510VEHICLE - STOLENNaN0NaNNaN101.0STREETNaNNaNAAAdult Arrest510.0NaNNaNNaNKENMORE STFOUNTAIN34.0953-118.2974
920012192912/04/2020 12:00:00 AM12/03/2020 12:00:00 AM23001Central1051510VEHICLE - STOLENNaN0NaNNaN101.0STREETNaNNaNICInvest Cont510.0NaNNaNNaN400 SOLANO AVNaN34.0710-118.2302
DR_NODate RptdDATE OCCTIME OCCAREAAREA NAMERpt Dist NoPart 1-2Crm CdCrm Cd DescMocodesVict AgeVict SexVict DescentPremis CdPremis DescWeapon Used CdWeapon DescStatusStatus DescCrm Cd 1Crm Cd 2Crm Cd 3Crm Cd 4LOCATIONCross StreetLATLON
113257125030405901/16/2025 12:00:00 AM01/16/2025 12:00:00 AM12003Southwest3381522VEHICLE, STOLEN - OTHER (MOTORIZED SCOOTERS, BIKES, ETC)NaN0NaNNaN121.0YARD (RESIDENTIAL/BUSINESS)NaNNaNICInvest Cont522.0NaNNaNNaN3100 MCCLINTOCK AVNaN34.0274-118.2850
113257225030414802/08/2025 12:00:00 AM02/06/2025 12:00:00 AM22153Southwest3251522VEHICLE, STOLEN - OTHER (MOTORIZED SCOOTERS, BIKES, ETC)NaN0NaNNaN101.0STREETNaNNaNICInvest Cont522.0NaNNaNNaN2600 S CATALINA STNaN34.0328-118.2942
113257325130409501/31/2025 12:00:00 AM01/30/2025 12:00:00 AM155413Newton13722850INDECENT EXPOSURENaN16FH101.0STREETNaNNaNICInvest Cont850.0NaNNaNNaN300 E 53RD STNaN33.9942-118.2701
113257425030403101/07/2025 12:00:00 AM01/02/2025 12:00:00 AM18323Southwest3121442SHOPLIFTING - PETTY THEFT ($950 & UNDER)0325 010424XX203.0OTHER BUSINESSNaNNaNICInvest Cont442.0NaNNaNNaN5100 W JEFFERSON BLNaN34.0255-118.3549
113257525050413802/10/2025 12:00:00 AM02/06/2025 12:00:00 AM305Harbor5231341THEFT-GRAND ($950.01 & OVER)EXCPT,GUNS,FOWL,LIVESTK,PRODNaN0NaNNaN101.0STREETNaNNaNICInvest Cont341.0NaNNaNNaN2000 JOHN S GIBSON BLNaN33.7566-118.2896
113257625100409201/25/2025 12:00:00 AM01/24/2025 12:00:00 AM124010West Valley10081331THEFT FROM MOTOR VEHICLE - GRAND ($950.01 AND OVER)1822 0344 038568MO108.0PARKING LOTNaNNaNICInvest Cont331.0NaNNaNNaN7600 WOODLEY AVNaN34.2085-118.4837
113257725170406601/17/2025 12:00:00 AM01/17/2025 12:00:00 AM160017Devonshire17742624BATTERY - SIMPLE ASSAULT0400 1259 1822 035617MH721.0HIGH SCHOOL400.0STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)ICInvest Cont624.0NaNNaNNaN9600 ZELZAH AVNaN34.2450-118.5233
113257825110408902/02/2025 12:00:00 AM01/30/2025 12:00:00 AM173011Northeast11011330BURGLARY FROM VEHICLE0344 1302 130732FH109.0PARK/PLAYGROUNDNaNNaNICInvest Cont330.0NaNNaNNaN2800 E OBSERVATORY RDNaN34.1192-118.3004
113257925210405301/19/2025 12:00:00 AM01/17/2025 12:00:00 AM153021Topanga21141341THEFT-GRAND ($950.01 & OVER)EXCPT,GUNS,FOWL,LIVESTK,PROD03440MW720.0JUNIOR HIGH SCHOOLNaNNaNICInvest Cont341.0NaNNaNNaN22200 ELKWOOD STNaN34.2128-118.6103
113258025050405101/14/2025 12:00:00 AM01/14/2025 12:00:00 AM12505Harbor5091210ROBBERY1822 0344 125915FH721.0HIGH SCHOOL400.0STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)ICInvest Cont210.0NaNNaNNaN24300 WESTERN AVNaN33.8046-118.3074